Abstract- Yuanjia Wang, Qinxia Wang, and Shanghong Xie - Columbia University
Title: Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
Title: Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
Abstract:
Abstract:
Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (two to three weeks after the outbreak). A fast rate of decline in was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the infection rate decreases. In the United States, significantly decreased during a 2-week period after the declaration of national emergency, but declines at a much slower rate afterwards. Since stopped decreasing further after mid-April and more states started to reopen in May, the reproduction number increased again, which resulted in a second surge of new cases. Since July, the disease spread has slowed down. If the decreasing trend continues, COVID-19 may be controlled by October with less than 100 daily new cases and a total of 5.6 million cases. However, a loss of temporal effect (e.g., due to relaxing mitigation measures) could lead to a long delay in controlling the epidemic and a total of more than 6.5 million cases. Our weekly realtime forecasts are included in the COVID ensemble modeling hub (https://viz.covid19forecasthub.org) and CDC forecasts of COVID-19 deaths (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html).
Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (two to three weeks after the outbreak). A fast rate of decline in was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the infection rate decreases. In the United States, significantly decreased during a 2-week period after the declaration of national emergency, but declines at a much slower rate afterwards. Since stopped decreasing further after mid-April and more states started to reopen in May, the reproduction number increased again, which resulted in a second surge of new cases. Since July, the disease spread has slowed down. If the decreasing trend continues, COVID-19 may be controlled by October with less than 100 daily new cases and a total of 5.6 million cases. However, a loss of temporal effect (e.g., due to relaxing mitigation measures) could lead to a long delay in controlling the epidemic and a total of more than 6.5 million cases. Our weekly realtime forecasts are included in the COVID ensemble modeling hub (https://viz.covid19forecasthub.org) and CDC forecasts of COVID-19 deaths (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html).